Acoustic Echo Control

Enzner, Gerald; Buchner, Herbert; Favrot, Alexis; Kuech, Fabian

doi:10.1016/b978-0-12-396501-1.00030-3

Cited by 83 publications

(63 citation statements)

References 53 publications

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“…However, in practice, the system to be identified could be variable in time. For example, in acoustic echo cancellation, it can be assumed that the impulse response of the echo path is modeled by a time-varying system following a first-order Markov model [20]. Therefore, a more reliable approach could be based on the Kalman filter, since the state variable model fits better in this context [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

An optimized NLMS algorithm for system identification

2016

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Section: Introductionmentioning

confidence: 99%

An optimized NLMS algorithm for system identification

2016

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“…The variance, 2 , captures the uncertainties in h( ). Equations (1) and (2) define a state variable model, similar to Kalman filtering [2], [13]. In this context, the main objective is to estimate or identify h( ) with an adaptive filter, defined byĥ( ) = [ĥ…”

Section: System Model and Nlms Algorithmmentioning

confidence: 99%

“…Moreover, we consider a state variable model in the development, assuming that the echo path is modeled by a time-varying system following a first-order Markov model, similar to Kalman filtering [2], [13]. The goal is to minimize the system misalignment, which represents the natural approach for this type of applications.…”

Section: Introductionmentioning

confidence: 99%

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An optimized NLMS algorithm for acoustic echo cancellation

Ciochină

Paleologu

Benesty

et al. 2015

2015 International Symposium on Signals, Circuits and Systems (ISSCS)

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In order to improve the overall performance of the normalized least-mean-square (NLMS) algorithm, there is the need to control its main parameters, i.e., the normalized stepsize and regularization terms. In this context, the variable stepsize and variable regularized versions of the NLMS algorithm are designed to address the conflicting requirement of fast convergence and low misadjustment. In this paper, we propose an optimized NLMS algorithm for acoustic echo cancellation (AEC). This algorithm is based on a joint-optimization on both the normalized step-size and regularization parameters, in the context of a state variable model (similar to Kalman filtering). The simulation results indicate that the proposed algorithm can be a reliable choice for AEC applications, since it achieves fast convergence and tracking, low misadjustment, and double-talk robustness.

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“…By general we mean that a block of time samples is considered at each iteration, instead of one time sample (as in the conventional approach). The main motivation behind this work is the appealing performance of the Kalman filter for echo cancellation [8], [9], [10], [11]. Also, the WL complex Kalman filters [12], [13], [14] were found to be attractive for many applications.…”

Section: Introductionmentioning

confidence: 99%

A Kalman filter for stereophonic acoustic echo cancellation

Paleologu

Benesty

Grant

et al. 2014

2014 48th Asilomar Conference on Signals, Systems and Computers

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The stereophonic acoustic echo cancellation (SAEC) scheme was recently recast by using the widely linear (WL) model, i.e., as a single-input/single-output system with complex random variables. In this paper, we present a Kalman filter with individual control factors in the context of the WL model for SAEC. As a specific feature, this algorithm uses a different level of uncertainty for each coefficient of the filter. Simulation results indicate that the proposed algorithm outperforms the recursive least-squares (RLS) algorithm, which is usually considered as the benchmark for SAEC.

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Acoustic Echo Control

Cited by 83 publications

References 53 publications

An optimized NLMS algorithm for system identification

An optimized NLMS algorithm for system identification

An optimized NLMS algorithm for acoustic echo cancellation

A Kalman filter for stereophonic acoustic echo cancellation

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